# Autonomous LLM post-training with Tunix on TPUs

DevFeed: [Autonomous LLM post-training with Tunix on TPUs](<https://devfeed.tech/articles/autonomous-llm-post-training-with-tunix-on-tpus-4205.md>)

Original publisher: [Read original article](<https://developers.googleblog.com/autonomous-llm-post-training-with-tunix-on-tpus/>)

Author: Wei Wei

Published: 2026-09-12T11:04:33.891311Z

Content type: article

Language: en

Sources: [Google Developers Blog](<https://devfeed.tech/sources/google-developers-blog.md>)

Topics: [post-training](<https://devfeed.tech/topics/post-training.md>), [AI Agent](<https://devfeed.tech/topics/ai-agent.md>), [Fine-tuning](<https://devfeed.tech/topics/fine-tuning.md>), [Large Language Model](<https://devfeed.tech/topics/llm.md>), [grpo](<https://devfeed.tech/topics/grpo.md>), [lora](<https://devfeed.tech/topics/lora.md>), [gemma](<https://devfeed.tech/topics/gemma.md>), [Google](<https://devfeed.tech/topics/google.md>), [Reinforcement learning](<https://devfeed.tech/topics/reinforcement-learning.md>), [dataset](<https://devfeed.tech/topics/dataset.md>), [Command-line interface](<https://devfeed.tech/topics/cli.md>)

Tags: [autonomous](<https://devfeed.tech/tags/autonomous.md>), [cli](<https://devfeed.tech/tags/cli.md>), [fine-tuning](<https://devfeed.tech/tags/fine-tuning.md>), [gemma](<https://devfeed.tech/tags/gemma.md>), [git](<https://devfeed.tech/tags/git.md>), [google](<https://devfeed.tech/tags/google.md>), [grpo](<https://devfeed.tech/tags/grpo.md>), [llm](<https://devfeed.tech/tags/llm.md>), [lora](<https://devfeed.tech/tags/lora.md>), [post-training](<https://devfeed.tech/tags/post-training.md>), [reinforcement-learning](<https://devfeed.tech/tags/reinforcement-learning.md>), [training](<https://devfeed.tech/tags/training.md>)

## AI overview

This article presents autofinetune, an autonomous research loop for LLM post-training. Using AI agents and Google's AI stack, including Tunix, Gemma, Cloud TPUs, Antigravity CLI, and Gemini Flash 3.7, it automates supervised fine-tuning and reinforcement learning with GRPO, exploring hyperparameters such as LoRA configurations, learning rates, batch sizes, and rollout settings.

## Source excerpt

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